Applying AI Algorithms to Big Data Governance: Use Cases and Future Directions

The article introduces how AI and big data mutually support each other and outlines five main sections.

1. Big Data and AI – Contrary to the common view that big data only serves AI, the piece explains that AI can also improve data collection, transmission, storage, processing, exchange, and destruction, enhancing efficiency, reducing cost, and ensuring security throughout the data lifecycle.

2. Data‑Task Health Assessment – A quantitative health‑score model is built using features such as runtime, resource usage, and failure count. Tasks are classified as good (1) or bad (0) and scored via XGBoost, providing owners with clear rankings and guidance for targeted governance.

3. Spark Task Intelligent Tuning – To reduce resource waste, a model recommends optimal values for executor cores, memory, and instance count. Two approaches are explored: learning from existing rule‑based recommendations with a multivariate regression model, and Bayesian optimization for global search. The solution achieves up to 15% improvement in resource utilization for most tasks.

4. SQL Engine Intelligent Selection – Based on SQL text features extracted via NLP (n‑gram TF‑IDF, linear filtering, XGBoost), the system predicts whether Presto or Spark is more suitable, automatically switches engines, and falls back on failover if needed, improving success rate and lowering cost.

5. Outlook – Future work includes semantic analysis of Spark jobs, classification‑based tuning for different scenarios, and engineering optimizations to address sample scarcity and testing costs.

The article also includes a Q&A section addressing the rule engine, variable selection, model combination, semantic analysis, safety measures for parameter recommendation, and references to related research.